I. Field
The present disclosure generally relates to systems and methods of processing data, and more particularly to systems and methods of processing vector and scalar operations.
II. Description of Related Art
Advances in technology have resulted in smaller and more powerful personal computing devices. For example, there currently exist a variety of portable personal computing devices, including wireless computing devices, such as portable wireless telephones, personal digital assistants (PDAs), and paging devices that are small, lightweight, and easily carried by users. More specifically, portable wireless telephones, such as cellular telephones and IP telephones, can communicate voice and data packets over wireless networks. Further, many such wireless telephones include other types of devices that are incorporated therein. For example, a wireless telephone can also include a digital still camera, a digital video camera, a digital recorder, and an audio file player. Also, such wireless telephones can include a web interface that can be used to access the Internet. As such, these wireless telephones include significant computing capabilities.
Typically, as these devices become smaller and more powerful, they become increasingly resource constrained. For example, the screen size, the amount of available memory and file system space, and the amount of input and output capabilities may be limited by the small size of the device. Further, the battery size, the amount of power provided by the battery, and the life of the battery is also limited. One way to increase the battery life of the device is to design less power consuming processors.
Certain types of processors employ a vector architecture for vector processing. Processors with a vector architecture provide high-level operations that work on vectors, i.e. linear arrays of data. Vector processing fetches an instruction once and then executes the instruction multiple times with different data. This allows the energy required to execute a program to be reduced because, among other factors, each instruction needs to be fetched fewer times. In addition, processors with a vector architecture usually allow multiple operations to be done at the same time, creating parallelism among the operations.
On the other hand, other types of processors employ a scalar architecture for scalar processing. Scalar processing fetches the instruction and data each time the instruction is executed. In executing a loop that requires an instruction be executed multiple times, a processor with a scalar architecture will fetch the instruction multiple times.
Vector processing is desirable for tasks that require the same operation to be performed on a large set of data. However, a processor with a vector architecture does not take into account scalar conditions or yield a scalar result. Scalar operations are useful when a processor has a linear scaling performance requirement, as in a video device expected to handle multiple video streams. For this reason, existing processors use a scalar architecture for multi-media processing. Due to the lack of parallelism, this approach requires the processor to run very quickly which is inefficient in terms of power consumption.
Accordingly, it would be advantageous to provide an improved processing system and method of processing vector operations that takes into account scalar conditions.